Session Six
Data visualisation with ggplot2
Akos Mate
2018 July
1. Data visualisation principles
Minimize noise, maximize signal in your graphs (or put it in other ways: maximize the data-ink ratio):
source: Darkhorse Analytics
- avoid chart junk
- Choose the type of plot depending on the type of data
- label chart elements properly and informatively
- ideally both x and y axis starts at 0 (scales can be really deceiving otherwise)
- use consistent units! (do not mix yearly and month GDP for example)
- ABSOLUTELY NO 3D PIE CHARTS. (When someone does 3D pie charts God makes a kitten cry.)
Me, seeing 3D charts (I am trigerred equally no matter the sub genre):
2. ggplot2
library(dplyr)
library(ggplot2)
library(ggridges)
library(ggthemes)
library(gapminder)# data
iris_df <- iris
summary(iris_df)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
#> 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
#> Median :5.800 Median :3.000 Median :4.350 Median :1.300
#> Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
#> 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
#> Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
#> Species
#> setosa :50
#> versicolor:50
#> virginica :50
#>
#>
#>
data("diamonds")
summary(diamonds)
#> carat cut color clarity
#> Min. :0.2000 Fair : 1610 D: 6775 SI1 :13065
#> 1st Qu.:0.4000 Good : 4906 E: 9797 VS2 :12258
#> Median :0.7000 Very Good:12082 F: 9542 SI2 : 9194
#> Mean :0.7979 Premium :13791 G:11292 VS1 : 8171
#> 3rd Qu.:1.0400 Ideal :21551 H: 8304 VVS2 : 5066
#> Max. :5.0100 I: 5422 VVS1 : 3655
#> J: 2808 (Other): 2531
#> depth table price x
#> Min. :43.00 Min. :43.00 Min. : 326 Min. : 0.000
#> 1st Qu.:61.00 1st Qu.:56.00 1st Qu.: 950 1st Qu.: 4.710
#> Median :61.80 Median :57.00 Median : 2401 Median : 5.700
#> Mean :61.75 Mean :57.46 Mean : 3933 Mean : 5.731
#> 3rd Qu.:62.50 3rd Qu.:59.00 3rd Qu.: 5324 3rd Qu.: 6.540
#> Max. :79.00 Max. :95.00 Max. :18823 Max. :10.740
#>
#> y z
#> Min. : 0.000 Min. : 0.000
#> 1st Qu.: 4.720 1st Qu.: 2.910
#> Median : 5.710 Median : 3.530
#> Mean : 5.735 Mean : 3.539
#> 3rd Qu.: 6.540 3rd Qu.: 4.040
#> Max. :58.900 Max. :31.800
#>
data(mpg)
summary(mpg)
#> manufacturer model displ year
#> Length:234 Length:234 Min. :1.600 Min. :1999
#> Class :character Class :character 1st Qu.:2.400 1st Qu.:1999
#> Mode :character Mode :character Median :3.300 Median :2004
#> Mean :3.472 Mean :2004
#> 3rd Qu.:4.600 3rd Qu.:2008
#> Max. :7.000 Max. :2008
#> cyl trans drv cty
#> Min. :4.000 Length:234 Length:234 Min. : 9.00
#> 1st Qu.:4.000 Class :character Class :character 1st Qu.:14.00
#> Median :6.000 Mode :character Mode :character Median :17.00
#> Mean :5.889 Mean :16.86
#> 3rd Qu.:8.000 3rd Qu.:19.00
#> Max. :8.000 Max. :35.00
#> hwy fl class
#> Min. :12.00 Length:234 Length:234
#> 1st Qu.:18.00 Class :character Class :character
#> Median :24.00 Mode :character Mode :character
#> Mean :23.44
#> 3rd Qu.:27.00
#> Max. :44.00
# sample our diamonds data, so it works faster
diamonds_df <- dplyr::sample_n(diamonds, 250)Refresher: syntax of the ggplot2 plots:
During this session we will go through various types of data visualisations and try to apply the above set principles to our output. Altough we already have some experience with various plots, during this session we will start modifying parts of our plot beyond the title and axis labels.
2.1 scatter plot
We use scatter plot to illustrate some association between two continuous variable. Usually, the y axis is our dependent variable (the variable which is explained) and x is the independent variable, which we suspect that drives the association. (this is not a stats course, but the mandatory: correlation != causation applies!).
Now, we want to know what is the association between the carat and price of a diamond.
ggplot(diamonds_df,
mapping = aes(x = carat,
y = price)) +
geom_point()
Now that we have a basic figure, let’s make it better.
ggplot(diamonds_df,
mapping = aes(x = carat,
y = price)) +
geom_point(aes(shape = cut)) # we can add additional dimensions, in this case we want to include info from the cut variable.
#> Warning: Using shapes for an ordinal variable is not advised
To add some analytical power to our plot we can use geom_smooth() and choose a method for it’s smoothing function. It can be lm, glm, gam, loess, and rlm. We will use the linear model (“lm”).
ggplot(diamonds_df,
mapping = aes(x = carat,
y = price)) +
geom_smooth(color = "orange", se = TRUE, method = "lm") + # adding the smoothed line. we can set the color, size and the `standard error` ribbon by the se argument
geom_point(alpha = 0.3) # setting the points' transparency
We can have colors by cut categories as well (as we did in the previous session)
ggplot(diamonds_df,
mapping = aes(x = carat,
y = price,
color = cut)) + # add the cut variable as color coding into our plot
geom_smooth(color = "black", se = TRUE, method = "lm") +
geom_point(alpha = 0.3)
We add horizontal line or vertical line to our plot, if we have a particular cutoff that we want to show. We can add these with the geom_hline() and geom_vline() functions.
ggplot(diamonds_df,
mapping = aes(x = carat,
y = price)) +
geom_point(aes(shape = cut)) +
geom_vline(xintercept = 2) + # adding vertical line
geom_hline(yintercept = 15000, linetype = "dashed", color = "orange", size = 0.5) # adding horizontal line, and modifying it a bit
#> Warning: Using shapes for an ordinal variable is not advised
2.2 histogram
Using histograms to check the distribution of your data.
ggplot(diamonds_df,
mapping = aes(x = carat)) +
geom_histogram() 
ggplot(diamonds_df,
mapping = aes(x = carat)) +
geom_histogram(binwidth = 0.1, color = "black", fill = "orange") # we can set the colors and border of the bars and set the binwidth or bins
We can overlay more than one histogram on each other. See how different iris species have different sepal length distribution.
ggplot(data = iris_df,
mapping = aes(x = Sepal.Length,
fill = Species)) +
geom_histogram(binwidth = 0.1, position = "identity", alpha = 0.65) # using the position option so we can see all three variables
2.3 density plots
A variation on histograms is called density plots that uses Kernel smoothing (fancy! but in reality is a smoothing function which uses the weighted averages of neighboring data points.)
ggplot(iris_df,
mapping = aes(x = Sepal.Length)) +
geom_density()
Add some fill
ggplot(iris_df,
mapping = aes(x = Sepal.Length)) +
geom_density(fill = "orange", alpha = 0.3)
Your intutition is correct, we can overlap this with our histogram
ggplot(diamonds_df,
mapping = aes(x = carat)) +
geom_histogram(aes(y = ..density..),
binwidth = 0.1,
fill = "white",
color = "black") +# we add this so the y axis is density instead of count.
geom_density(alpha = 0.25, fill = "orange")
And similarly to the historgram, we can overlay two density plot as well.
ggplot(iris_df,
mapping = aes(x = Sepal.Length,
fill = Species)) +
geom_density(alpha = 0.5)
2.3.1 ridgeline/joyplot
This one is quite spectacular looking and informative. It has a similar function as the overlayed histograms but presents a much clearer data. For this, we need the ggridges package which is a ggplot2 extension.
ggplot(data = iris_df,
mapping = aes(x = Sepal.Length,
y = Species)) +
geom_density_ridges(scale = 0.8, alpha = 0.5)
2.4 bar charts
We can use the bar charts to visualise categorical data.
ggplot(data = mpg,
mapping = aes(x = class)) +
geom_bar()
We can use the fill option to map another variable onto our plot. Let’s see how these categories are further divided by the type of transmission (front, rear, 4wd). By default we get a stacked bar chart.
ggplot(data = mpg,
mapping = aes(x = class,
fill = drv)) +
geom_bar()
we can use the position function in the geom_bar to change this.
ggplot(data = mpg,
mapping = aes(x = class,
fill = drv)) +
geom_bar(position = "dodge")
Let’s make sure that the bars are proportional. For this we can use the y = ..prop.. and group = 1 arguments, so the y axis will be calculated as proportions. The ..prop.. is a temporary variable that has the .. surrounding it so there is no collision with a variable named prop.
ggplot(data = mpg,
mapping = aes(x = class)) +
geom_bar(aes(y = ..prop.., group = 1))
ggplot(data = mpg,
mapping = aes(x = class,
fill = drv)) +
geom_bar(position = "dodge",
mapping = aes(y = ..prop.., group = drv),
color = "black")
Maybe it is best to facet by drv.
ggplot(data = mpg,
mapping = aes(x = class)) +
geom_bar(position = "dodge",
mapping = aes(y = ..prop.., group = drv),
color = "black") +
facet_wrap(~drv, ncol = 2)
2.4.1 Lollipop charts
The lollipop chart is a better barchart in a sense that it conveys the same information with better data/ink ratio. It also looks better.
For this we will modify a chart from the Data Visualisation textbook
load("oecd_sum.rda")
p <- ggplot(data = oecd_sum,
mapping = aes(x = year, y = diff, color = hi_lo))
p + geom_segment(aes(y = 0, x = year, yend = diff, xend = year)) +
geom_point() +
theme(legend.position="none") +
labs(x = NULL, y = "Difference in Years",
title = "The US Life Expectancy Gap",
subtitle = "Difference between US and OECD
average life expectancies, 1960-2015",
caption = "Adapted from Kieran Healy: Data Visualisation, fig.4.21 ")
#> Warning: Removed 1 rows containing missing values (geom_segment).
#> Warning: Removed 1 rows containing missing values (geom_point).
2.5 box plot
ggplot(data = iris_df,
mapping = aes(x = Species,
y = Sepal.Length)) +
geom_boxplot()
We add color coding to our boxplots as well.
ggplot(data = iris_df,
mapping = aes(x = Species,
y = Sepal.Length,
fill = Species)) +
geom_boxplot(alpha = 0.5)
2.6 violin chart
ggplot(data = iris_df,
mapping = aes(x = Species,
y = Sepal.Length)) +
geom_violin()
3. Themes and plot elements
3.1 Themes
In this section we will go over some of the elements that you can modify in order to get an informative and nice looking figure. ggplot2 comes with a number of themes. You can play around the themes that come with ggplot2 and you can also take a look at the ggthemes package, where I included the economist theme. Another notable theme is the hrbthemes package.
The black and white theme. Try out a couple to see what they differ in! The ggthemes package has a nice collection of themes to use.
ggplot(data = diamonds_df,
mapping = aes(x = carat,
y = price)) +
geom_point() +
labs(title = "theme_bw") +
theme_bw()
3.2 Plot elements
Of course we can set all elements to suit our need, without using someone else’s theme.
The key plot elements that we will look at are:
- labels
- gridlines
- fonts
- colors
- legend
- axis breaks
Adding labels, title, as we did before.
ggplot(diamonds_df,
mapping = aes(x = carat,
y = price,
color = cut)) +
geom_point(alpha = 0.3) +
labs(title = "Diamonds are our best friends",
subtitle = "Connection between the carat and the price of diamonds",
x = "Carat",
y = "Price (USD)",
color = "Cut") # title of the legend
Let’s use a different color scale! We can use a color brewer scale (widely used for data visualization).
ggplot(diamonds_df,
mapping = aes(x = carat,
y = price,
color = cut)) +
geom_point(alpha = 0.3) +
scale_color_brewer(name = "Cut", palette = "Set1") + # adding the color brewer color scale
labs(title = "Diamonds are our best friends",
subtitle = "Connection between the carat and the price of diamonds",
x = "Carat",
y = "Price (USD)",
color = "Cut")
Or we can define our own colors:
ggplot(diamonds_df,
mapping = aes(x = carat,
y = price,
color = cut)) +
geom_point(alpha = 0.3) +
scale_color_manual(values=c("red", "blue", "orange", "black", "green")) + # adding our manual color scale
labs(title = "Diamonds are our best friends",
subtitle = "Connection between the carat and the price of diamonds",
x = "Carat",
y = "Price (USD)",
color = "Cut")
To clean up clutter, we will remove the background, and only leave some of the grid behind. We can hide the tickmarks with modifying the theme() function, and setting the axis.ticks to element_blank().
ggplot(diamonds_df,
mapping = aes(x = carat,
y = price,
color = cut)) +
geom_point(alpha = 0.3) +
labs(title = "Diamonds are our best friends",
subtitle = "Connection between the carat and the price of diamonds",
x = "Carat",
y = "Price (USD)") +
theme(axis.ticks = element_blank()) # removing axis ticks
Hiding gridlines also requires some digging in the theme() function with the panel.grid.minor or .major functions. If you want to remove a gridline on a certain axis, you can specify panel.grid.major.x. We can also set the background to nothing. Furthermore, we can define the text attributes as well in our labels.
ggplot(diamonds_df,
mapping = aes(x = carat,
y = price,
color = cut)) +
geom_point(alpha = 0.3) +
labs(title = "Diamonds are our best friends",
subtitle = "Connection between the carat and the price of diamonds",
x = "Carat",
y = "Price (USD)") +
theme(axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) # removing the background 
ggplot(diamonds_df,
mapping = aes(x = carat,
y = price,
color = cut)) +
geom_point(alpha = 0.3) +
labs(title = "Diamonds are our best friends",
subtitle = "Connection between the carat and the price of diamonds",
x = "Carat",
y = "Price (USD)") +
theme(axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.title = element_text(size = 12), # setting the legends text size
text = element_text(face = "plain", family = "serif")) # setting global text options for our plot
Finally, let’s move the legend around. Or just remove it with theme(legend.position="none")
ggplot(diamonds_df,
mapping = aes(x = carat,
y = price,
color = cut)) +
geom_point(alpha = 0.3) +
labs(title = "Diamonds are our best friends",
subtitle = "Connection between the carat and the price of diamonds",
x = "Carat",
y = "Price (USD)") +
theme(axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.title = element_text(size = 12),
text = element_text(face = "plain", family = "serif"),
legend.background = element_blank(),
legend.position = "bottom")
While we are at it, we want to have labels for our data. For this, we’ll create a plot which can exploit this.
What we use is the geom_text to have out labels in the chart.
gapminder <- gapminder %>%
filter(year == 2002, continent == "Europe")
ggplot(gapminder, aes(lifeExp, gdpPercap, label = country)) + # we add the labels!
geom_point() +
geom_text() # and use the geom text
notice the different outcome of geom_label instead of geom_text.
ggplot(gapminder, aes(lifeExp, gdpPercap, label = country)) + # we add the labels!
geom_point() +
geom_label() # and use the geom label
If we want to label a specific set of countries we can do it from inside ggplot, without needing to touch our data.
ggplot(gapminder, aes(lifeExp, gdpPercap, label = country)) + # we add the labels!
geom_point() +
geom_text(aes(label = if_else(lifeExp > 80, country, NULL)), nudge_x = 0.5) # we add a conditional within the geom. Note the nudge_x!
#> Warning: Removed 26 rows containing missing values (geom_text).